Pulp supply optimization – Case M-real

The objective of this study was to develop a pulp supply optimization model, a management decision support tool, which would efficiently optimize the allocation of the pulp within the company’s supply chain in such a way that the company’s added value is maximized. The company’s current situation and operational methods were carefully analyzed in order to build an accurate linear model that would optimize pulp allocation from the company’s pulp mills and external pulp suppliers to the company’s own paper mills and external customers. A thorough investigation of previous studies in the field of supply chain management, and production and distribution optimization was completed and utilized in order to find the best methods for the modeling purposes of this study. Using the commercial software Excel and its solver, a linear pulp supply optimization model was then built to optimize the pulp supply from the suppliers to the customers. Company data was collected and used in the model to run four different scenarios that would help answering the research questions presented in the study. From the study’s perspective, the most interesting question was whether the company’s added value could be increased by utilizing the developed optimization model instead of current decision methods. The results of the optimal solution were compared to the current supply plan under two different scenarios: one without predetermined allocation constraints and one with allocation constraints. The first case illustrated the best case scenario, whereas the second case illustrated the more realistic case. Two additional scenarios in the study were related to the models utility as an information source. In the study it was found that significant improvements in terms of added value can be achieved by restructuring the pulp supply in a way the model suggests. Indeed, a 6.6 percent increase in added value was found to be possible in the optimal case, and even in the more realistic case the increase was a notable 5.5 percent. This increase was found to be induced by the model’s ability to handle the complexities of the product properties and demand characteristics in a way that the company’s internal capacity was utilized in the most efficient way. Also the model’s ability to function as a powerful scenario tester and information source was proven in the cases where effects of the market price changes on added value, and the opportunity costs of product certifications were studied. The goals of the study were achieved very well and the built pulp supply optimization model developed in this study proved to be successful and visibly revealed the weaknesses of the current decision methods in use. The vast amount of decision variables and the complex and continuously changing nature of the demand characteristics make the pulp supply coordination a very difficult task without computational help, as was shown in this study